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• W4311484994 startingPage "101867" @default.
• W4311484994 abstract "Stock index futures allows stock investors to manage different kinds of risk. This paper combines the AdaBoost feature selection and deep learning model for predicting stock index futures prices. In particular, a hybrid model is proposed in which the sklearn wrapped AdaBoost regressor is used for feature selection and the two-layer long short-term memory-based predictor is constructed. Performance metrics consistently show that the proposed model outperforms other popular prediction models such as random forest, multi-layer perception, gated recurrent unit, deep belief network and stacked denoising autoencoder." @default.
• W4311484994 created "2022-12-26" @default.
• W4311484994 creator A5053663262 @default.
• W4311484994 date "2023-01-01" @default.
• W4311484994 modified "2023-10-09" @default.
• W4311484994 title "Stock index futures price prediction using feature selection and deep learning" @default.
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• W4311484994 doi "https://doi.org/10.1016/j.najef.2022.101867" @default.
• W4311484994 hasPublicationYear "2023" @default.
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